Author

Christian Blumenscheit

Robert Koch InstituteORCID 0000-0001-8987-2355Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:human· 2 Assay:proteomics· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Research Focus:host-pathogen & immune response· 1 Assay:metagenomics· 1

Frequent Co-Authors

Andy SchneideriD· 2 shared Joerg DoellingeriD· 2 shared Peter LaschiD· 2 shared Anne-Kathrin Liebschner· 1 shared Astrid BetheiD· 1 shared Birgit WaltheriD· 1 shared C Förster· 1 shared Charlyn JohniD· 1 shared

Activity by Year

2020-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersMicrobiology

Andy SchneiderORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

MicrobiologybioRxivPreprintMar 2, 2026

Bridging disciplines towards wastewater-based surveillance of antimicrobial resistance: frequency, local dynamics, and genomic characteristics of carbapenemase-producing Klebsiella pneumoniae

Katharina A. Werner, Vladimir Bajić, Christian Blumenscheit…·Birgit Walther

This paper develops a workflow for detecting carbapenemase-producing Klebsiella pneumoniae in wastewater using wet-lab methods and genomics, finding high prevalence and specific genetic markers.

DOI 10.64898/2026.03.02.707721

humanmetagenomics

𝕏 PostBluesky

MicrobiologyMolecular & Cellular ProteomicsPeer-reviewedFeb 26, 2026

Antibiotic resistance detection and concomitant species identification of ESKAPE pathogens by proteomics

Christian Blumenscheit, Yvonne Pfeifer, Guido Werner…·Joerg Doellinger

This study evaluates proteomics for rapid detection of antibiotic resistance and species identification of ESKAPE pathogens from clinical isolates, achieving high specificity and sensitivity.

1 citation

bacteria (other)proteomics

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Systems BiologyAnalytical ChemistryPeer-reviewedAug 25, 2020

Optimization of data-independent acquisition using predicted libraries for deep and accurate proteome profiling

Joerg Doellinger, Christian Blumenscheit, Andy Schneider, Peter Lasch

The paper optimizes data-independent acquisition (DIA) proteomics by combining in silico libraries, advanced separation, and neural network analysis to achieve high coverage and accuracy.

37 citations

humanproteomics

𝕏 PostBluesky